Sectors Performance
Sector Price Performance Distribution
For Date: 2026-08-14
Performance Heatmap (%)
| Sector | 1 Day | 1 Week | 1 Month | 3 Months | 6 Months | YTD | 1 Year |
| Energy | 1.39 | 2.87 | 8.71 | 7.37 | 16.77 | 37.49 | 49.22 |
| Utilities | 0.61 | 2.74 | -3.02 | -0.69 | -3.19 | 3.99 | 6.06 |
| Materials | 0.44 | -1.20 | 3.75 | 2.06 | 0.52 | 14.84 | 18.82 |
| Industrials | 0.39 | 1.03 | 3.36 | 7.13 | 7.09 | 18.68 | 24.99 |
| Communication Services | 0.36 | 1.00 | 1.35 | -3.30 | -0.70 | -2.82 | 2.85 |
| Real Estate | 0.33 | 1.96 | 1.78 | 4.00 | 4.62 | 13.86 | 14.06 |
| Consumer Staples | 0.10 | 1.34 | 3.20 | 2.00 | -1.16 | 12.20 | 8.04 |
| Financials | -0.17 | 0.61 | 3.52 | 13.79 | 12.39 | 6.79 | 11.42 |
| Consumer Discretionary | -0.21 | -1.23 | 1.98 | -0.20 | 2.27 | 0.27 | 3.77 |
| Technology | -0.40 | 1.98 | 3.48 | 5.99 | 36.56 | 32.00 | 42.68 |
| Health Care | -0.60 | -0.64 | 5.74 | 14.65 | 7.26 | 8.55 | 27.11 |
Ask the market a question. Get a calculated answer.
The AI is not a chatbot bolted onto a document store. It calls the same analytics engine that powers every screen on this platform — so what comes back is a number it computed from raw history, with the command that produced it.
86,000+ instruments
Global equities, ETFs, funds, options, FX, commodities, crypto, economics, filings, transcripts and news — one normalised symbol universe with adjusted history.
A real analytics engine
Screening, backtesting, technicals, options analytics, correlations, seasonality and factor models — computed on demand from raw prices, never a stale cache.
It shows its working
Answers arrive with the charts, tables and tool calls behind them, so you can check the number instead of trusting a paraphrase.
Your own documents
Upload filings, decks and research. Ask across them and the answer cites the page it came from.
Agents and workflows
Multi-step research that runs the platform's tools for you — screen, pull the history, compute, compare, then write it up.
MCP, CLI and API
The same command catalogue from Claude, your own agent, a shell or your pipeline. The answer on screen is the answer your job gets at 4am.
You ask
“How does NVDA usually trade through earnings?”
It calls
→ ka.options_expected_move(NVDA)
It answers
NVDA has averaged a 9.2% absolute move on the day after earnings and closed higher 67% of the time. Two in three reactions land between −4.2% and +16.3% — the distribution is skewed right, not symmetric.
Every figure computed live from our own history — not scraped, not summarised.
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